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Title: Neural Network Based Machine Condition Monitoring System 
Author: Kaneshige, Kenichi
Date: 2004-05
Abstract: In this paper, machine condition monitoring techniques based on multilayered feedfoward neural network (MLFFNN) where the weights in the network are updated based on nodedecoupled extended Kalman filter (NDEKF) training method are proposed. Neural network based techniques have been widely recognized as powerful approaches for condition monitoring system, and the use of NDEKF has better performances in computational complexity and memory requirement among the Kalman filtering algorithm family. The condition monitoring system detects and identifies conditions of components through the neural network based system identification of components. Sensor signals in both time and frequency domains are analyzed to show the effectiveness of the condition monitoring scheme. The performances of diagnostic tools presented in this thesis are evaluated using the cabin temperature control system that is specifically for Boeing 767 as practical application example, and the results show the effectiveness of the developed techniques.
URI: http://hdl.handle.net/10125/10441
Rights: All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.

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